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Development of a novel combined nomogram integrating deep-learning-assisted CT texture and clinical-radiological features to predict the invasiveness of clinical stage IA part-solid lung adenocarcinoma: a multicentre study.
Zuo, Z; Zeng, W; Peng, K; Mao, Y; Wu, Y; Zhou, Y; Qi, W.
Affiliation
  • Zuo Z; Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan 411000, China.
  • Zeng W; Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan 411000, China.
  • Peng K; Department of Spine Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China.
  • Mao Y; Department of Radiology, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha, Hunan 410004, China.
  • Wu Y; Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan 411000, China.
  • Zhou Y; Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan 411000, China.
  • Qi W; Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan 646100, China. Electronic address: qiwanyin0508@163.com.
Clin Radiol ; 78(10): e698-e706, 2023 10.
Article in En | MEDLINE | ID: mdl-37487842
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Adenocarcinoma of Lung / Deep Learning / Lung Neoplasms Type of study: Observational_studies / Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Clin Radiol Year: 2023 Type: Article Affiliation country: China
Fulltext
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Adenocarcinoma of Lung / Deep Learning / Lung Neoplasms Type of study: Observational_studies / Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Clin Radiol Year: 2023 Type: Article Affiliation country: China